Abstract
This paper presents an extension of the Self Organizing Map model called Associative SOM that is able to process different types of input data in separated data-paths. The ASOM model can easily deal with situations of incomplete data-patterns and incorporate class labels for supervisory purposes. The ASOM is successfully compared with Multilayer Perceptrons in the incremental classification of six erythemato-squamous diseases, where only partial data is available in successive steps.
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del-Hoyo, R., Buldain, D., Marco, A. (2003). Supervised Classification with Associative SOM. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_43
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DOI: https://doi.org/10.1007/3-540-44868-3_43
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